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A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)
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[article]
Titre : A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods Type de document : Article/Communication Auteurs : Pengxiang Zhao, Auteur ; Zohreh Masoumi, Auteur ; Maryam Kalantari, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] cartographie des risques
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] effondrement de terrain
[Termes IGN] Iran
[Termes IGN] modèle numérique de surface
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] risque naturel
[Termes IGN] système d'information géographiqueRésumé : (auteur) Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management. Numéro de notice : A2022-056 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/rs14010211 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.3390/rs14010211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99459
in Remote sensing > vol 14 n° 1 (January-1 2022) . - n° 211[article]Global and climate challenges, graph-based data analysis for multisource information extraction / Morgane Batelier (2022)
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Titre : Global and climate challenges, graph-based data analysis for multisource information extraction Type de document : Mémoire Auteurs : Morgane Batelier, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 43 p. Format : 21 x 30 cm Note générale : Bibliographie
Mémoire de fin d'études, cycle des ingénieurs ENSG 3ème année, FRSLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Arctique, océan
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] glace de mer
[Termes IGN] image hyperspectrale
[Termes IGN] image Sentinel-SAR
[Termes IGN] polarimétrie radar
[Termes IGN] traitement d'image radarIndex. décimale : MPT Mémoires de fin d'études du Master Méthodes physiques en télédétection Résumé : (Auteur) During my end-of-studies internship, I worked on the development of a label propagation algorithm for remote sensing data, using Deep Learning. It was mainly applied to sea ice classification using SAR Sentinel-1 data, and to hyperspectral imaging in order to be effective to multimodal remote sensing. I started by the bibliography, during which we decided with my supervisors the method I was going to work from. Then, I worked on the algorithm implementation that was the longest phase. Finally, the last part of my work was the certification and improvement of the results using different process. Note de contenu : Introduction
1. Remote Sensing in the Arctic
1.1 Challenges of the Arctic
1.2 Sea Ice
2. Label Propagation for Deep Learning
2.1 Preliminaries
2.2 Transductive Propagation Network for Few-shot Learning
3. Multimodal Remote Sensing Data
3.1 Synthetic Aperture Radar
3.2 Hyperspectral Imaging
4. Experimental results
4.1 Datasets
4.2 Improvement Methods
4.3 Discussion and future of the algorithm
ConclusionNuméro de notice : 26935 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : Center for Integrated Remote Sensing and Forecasting for Arctic Operations CIRFA Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102059 Documents numériques
en open access
Global and climate challenges, graph-based data analysis for multisource information extraction - pdf auteurAdobe Acrobat PDFGlobal canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)
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Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] biomasse aérienne
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation bayesienne
[Termes IGN] forme d'onde
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495
in Remote sensing of environment > vol 268 (January 2022) . - n* 112760[article]High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach / Martin Schwartz (2022)
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Titre : High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach Type de document : Article/Communication Auteurs : Martin Schwartz, Auteur ; Philippe Ciais, Auteur ; Catherine Ottle, Auteur ; Aurélien de Truchis, Auteur ; Cédric Vega , Auteur ; Ibrahim Fayad, Auteur ; Martin Brandt, Auteur ; Rasmus Fensholt, Auteur ; Nicolas Baghdadi, Auteur ; François Morneau
, Auteur ; David Morin, Auteur ; Dominique Guyon, Auteur ; Sylvia Dayau, Auteur ; Jean-Pierre Wigneron, Auteur
Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] forêt
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] hauteur des arbres
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Landes de Gascogne
[Termes IGN] PinophytaRésumé : (auteur) In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km2 with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region. Numéro de notice : P2022-002 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2212.10265 Date de publication en ligne : 20/12/2022 En ligne : https://doi.org/10.48550/arXiv.2212.10265 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102850 Incorporation of spatial anisotropy in urban expansion modelling with cellular automata / Jinqu Zhang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)
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Titre : Incorporation of spatial anisotropy in urban expansion modelling with cellular automata Type de document : Article/Communication Auteurs : Jinqu Zhang, Auteur ; Yu Ling, Auteur ; A - Xing Zhu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 86 - 113 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] anisotropie
[Termes IGN] automate cellulaire
[Termes IGN] croissance urbaine
[Termes IGN] Kouangtoung (Chine)
[Termes IGN] modèle de simulation
[Termes IGN] régression logistique
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Cellular Automata (CA) models have become the most commonly used tool for simulating urban expansion. To improve the accuracy of CA models, various driving factors like spatial proximity and neighbourhood effects have been explored in previous studies, but the inclusion of these factors does not address the directional differences in urban expansion. To address this issue, this study develops a method to measure urban spatial anisotropy (SA) with respect to 18 variables at both the global and local scales, and integrates all these SA variables into a logistic regression-based CA model. The revised CA model is evaluated with a case study for Huizhou, China. The case study shows that the simulation results for the CA model with SA exhibit 89% overall accuracy; compared to CA models that do not consider SA, the revised CA model can improve precision by 5% on newly developed cells. The consideration of SA in CA models proves promising in improving the accuracy of urban expansion simulations. Numéro de notice : A2022-044 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1852475 Date de publication en ligne : 30/11/2020 En ligne : https://doi.org/10.1080/13658816.2020.1852475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99403
in International journal of geographical information science IJGIS > vol 36 n° 1 (January 2022) . - pp 86 - 113[article]Réservation
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